Formal structure-based algorithms for large scale resource scheduling optimization

Abstract

A method and computer program product for optimization of large scale resource scheduling problems. Large scale resource scheduling problems are computationally very hard and extremely time consuming to solve. This invention provides a Lagrangian relaxation based solution method. The method has two distinct characteristics. First, the method is formal. It is completely structure-based and does not use any problem domain specific knowledge in the solution process, either in the dual optimization or the primal feasibility enforcement process. Second, updating the Lagrangian multipliers after solution of every sub-problem without using penalty factors results in fast and smooth convergence in the dual optimization. The combination of high quality dual solution and the structure-based primal feasibility enforcement produces a high quality primal solution with very small solution gap. An optimal solution is first found to the dual of the resource scheduling problem by sequentially finding a solution to a plurality of sub-problems and updating a set of values used in the dual problem formulation after each sub-problem solution is obtained. Coupling constraint violations are systematically reduced and the set of values are updated until a feasible solution to the primal resource scheduling problem is obtained. An initial set of multiplier values is further determined by solving a relaxed version of the primal problem where most of the local constraints except the variable bounds are relaxed.

Description

FIELD OF THE INVENTION[0001]The present invention relates generally to large scale resource scheduling optimization, and more particularly, to general structure-based methods that are applicable to large scale resource scheduling optimization.BACKGROUND OF THE INVENTION[0002]Resource scheduling is a special class of optimization problem, in which a mix of available resources are utilized to satisfy demand side requirements over a given time horizon at prescribed temporal or spatial resolutions. The decisions to be made involve the determination of optimal operation schedules for all resources involved. The operation schedule for a resource can be described by the startup and shutdown, and utilization at each time step over the scheduling horizon. The operation schedules for all the resources are determined such that various local and global constraints are satisfied and some generalized cost function is minimized. A well-known classic example of resource scheduling problem is the so...

AI Technical Summary

Benefits of technology

[0007]The present invention provides a complete solution process for large-scale resource scheduling optimization and has the following unique advantages:

[0009]2. the solution provides very fast dual optimization—obtaining a high quality dual solution in just a few iterations, the invention results in drastic execution speed improvement over other methods;

[0010]3. the solution uses structure-based heuristics to search for primal feasibility—no problem formulation specific heuristics are required, thus increasing reusability for other resource scheduling problems; and

[0013]The structure-based algorithm of the invention is particularly well-suited for large scale resource scheduling such as the unit commitment problem in the electric power industry wherein hydro and thermal generating units are scheduled for operation to meet demand over a period of time while minimizing total operating costs. In the simplest case, the scheduling problem is decomposed into a single sub-problem for each thermal plant and for each hydroelectric plant.

Problems solved by technology

The resource scheduling problem, by its combinatorial nature, is very hard.

The computational burden increases exponentially with the number of resources and the time steps in the scheduling horizon.

Speed is how fast the method is at finding the solution.

This is an iterative process plagued by slow convergence, requiring hundreds, sometimes thousands of iterations.

Quickly getting to the optimal dual solution is an attractive and challenging objective in the design of Lagrangian relaxation-based algorithms.

The primal solution corresponding to the optimal dual solution in general is not ensured to be feasible for all the coupling constraints.

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